The present invention relates to machine vision and more particularly to a machine vision method and apparatus for inspecting semiconductor die assemblies.
Integrated circuits are typically assembled using automatic assembly equipment to pick and place a semiconductor die onto a lead frame component where it is usually secured by an adhesive such as epoxy. This process is called die bonding. The lead frame component includes conductive leads that provide external connections to the die. Automatic equipment bonds conductive wires between pads on the die and leads on the lead frame. Precise wire-bonding operations rapidly connect a large number of very small and closely spaced pads to respective leads on the lead frame. Once a wire bonding operation is complete, the die and connective wires are typically encapsulated or enclosed in an insulating package.
Semiconductor die assembly components and manufacturing operations are very expensive so it is important to inspect the surfaces of the semiconductor die after die bonding. This process is called post bond inspection. Deposits of unwanted adhesive on the die are among the most commonly occurring die bonding defects. Such adhesive deposits can effectively xe2x80x9cshort circuitxe2x80x9d the semiconductor die""s electronic functions because the adhesive is typically conductive. Electrical testing of a final assembly would detect such short circuits but only after significant additional manufacturing costs have been incurred.
The inspection of semiconductor dies for adhesive has proven to be a vexing machine vision problem. This is a result of the complexity of the background, i.e., the circuitry pattern etched into the layers of the die. Furthermore a die may be rotated and/or shifted by a significant amount when the die and adhesive are placed onto the lead frame, due to lack of fixturing. Such rotation or shifting complicates inspection by a machine vision system because the machine vision system must first find the die.
A semiconductor die is not generally fixtured when it is assembled to a lead frame so it must be first located by any machine vision inspection system prior to its inspection. Machine vision inspection systems must typically perform an alignment procedure to compensate for shifting and rotation prior to performing an inspection operation. Alignment procedures according to the prior art, like the ones using normalized correlation, are configured to only find translation (2 D.O.F.). To find rotation, two models are trained, typically as far apart as possible, to increase accuracy. By determining the two positions, the rotation component can be computed. However, if the rotation is excessive the position may not be found. This procedure involves training a model in a reference image and then finding it within a region of interest in the runtime image. Fiducials may be provided on the reference object to increase the accuracy and efficiency of the alignment procedure. An alignment procedure that is used in semiconductor die inspection typically recognizes and locates two opposite corners of a die under inspection and two opposite corners of the lead frame. The machine vision inspection system can then compute the shift and rotation of the semiconductor die relative to its train-time position.
Methods according to the prior art have limited tolerance to rotation. For example certain of such methods can tolerate up to 10 degrees or 15 degrees of rotation but at the cost of reduced accuracy. Alternatively, numerous incrementally rotated reference images could be stored, but such methods are inefficient because they consume excessive memory and processor resources. Such methods decrease system accuracy and are inefficient because they consume excessive memory and processor resources.
The prior art suggests the use of a technique referred to as golden template comparison (GTC) to inspect die surfaces. GTC is a technique for locating objects by comparing a feature under scrutiny (to wit, a die surface) to a good imagexe2x80x94or golden templatexe2x80x94that is stored in memory. The technique subtracts the good image from the test image and analyzes the difference to determine if the unexpected object (e.g., a defect) is present. For example, upon subtracting the image of a good die surface from a defective one, the resulting xe2x80x9cdifferencexe2x80x9d image would reveal an adhesive blotch that could be flagged as a defect.
Before GTC inspections can be performed, the system must be xe2x80x9ctrainedxe2x80x9d so that the golden template can be stored in memory. To this end, the GTC training functions are employed to analyze several good samples of a scene to create a xe2x80x9cmeanxe2x80x9d image and xe2x80x9cstandard deviationxe2x80x9d image. The mean image is a statistical average of all the samples analyzed by the training functions. It defines what a typical good scene looks like. The standard deviation image defines those areas on the object where there is little variation from part to part, as well as those areas in which there is great variation from part to part. This latter image permits GTC""s runtime inspection functions to use less sensitivity in areas of greater expected variation, and more sensitivity in areas of less expected variation.
At runtime, a system employing GTC captures an image of a scene of interest. Where the position of that scene is different from the training position, the captured image is aligned, or registered, with the mean image. The intensities of the captured image are also normalized with those of the mean image to ensure that the variations in illumination do not adversely affect the comparison.
The GTC inspection functions then subtract the registered, normalized, captured image that contains all the variations between the two. That difference image is then compared with at xe2x80x9cthresholdxe2x80x9d image derived from the standard deviation image. This determines which pixels of the difference image are to be ignored and which should be analyzed as possible defects. The latter are subjected to morphology, to eliminate or accentuate pixel data patterns and to eliminate noise. An object recognition technique, such as connectivity analysis, can then be employed to classify the apparent defects.
Although GTC inspection tools have proven quite successful, they suffer some limitations. For example, except in unusual circumstances, GTC requires registrationxe2x80x94i.e., that the image under inspection be registered with the template image. GTC also used a standard deviation image for thresholding, which can result in a loss of resolution near edges due to high resulting threshold values. GTC is, additionally, limited to applications where the images are repeatable: it cannot be used where image-to-image variation results from changes in size, shape, orientation and warping. Furthermore GTC methods disadvantageously tolerate rotation only by inefficiently storing a set of rotated referenced images as previously described.
In application to die surface inspection, GTC is further limited because its fixed template typically does not include the die edges because edges are not generally repeatable with the necessary precision from die-to die due to manufacturing machinery sawing tolerances. It is here, however, that the probability of deposited adhesive is very high. Moreover, the complexity of the etching patterns on the die surfaces results in large area being effectively masked by the high standard deviation. Therefore, GTC methods are generally incapable of inspecting the fine detail of typical semiconductor die surfaces.
An improved method of inspecting semiconductor dies is taught in U.S. Pat. No. 5,949,901 which is incorporated herein by reference in its entirety. The method according to U.S. Pat. No. 5,949,901 (the ""901 invention) includes the steps of generating a first image of the die (including, the patterns etched into its surface and any other structuresxe2x80x94together, referred to as the xe2x80x9cdie,xe2x80x9d or xe2x80x9cdie surfacexe2x80x9d or xe2x80x9cbackgroundxe2x80x9d), generating a second image of the die and any defects thereon, and subtracting the second image from the first image. The method is characterized in that the second image is generated such that subtraction of it from the first image emphasizes a defect (e.g., excessive adhesive) with respect to the die or background.
In related aspects of the ""901 invention, the second step is characterized as generating the second image such that its subtraction from the first image increases a contrast between the defect and the background. That step is characterized in still further aspects of the ""901 invention, as being one that results in defect-to background contrast differences in the second image that are opposite polarity from the defect-to-defect contrast differences in the first image.
In further aspects, the ""901 invention calls for generating a third image with the results of the subtraction and for isolating the expected defects on that third image. Isolation can be performed according to other aspects of the ""901 invention, by conventional machine vision segmentation techniques such as connectivity analysis, edge detection and/or tracking, and by thresholding. In the latter regard, a threshold imagexe2x80x94as opposed to one or two threshold valuesxe2x80x94can be generated by mapping image intensity values of the first or second image. That threshold image can then be subtracted from the third image (i.e., the difference image) to isolate further the expected defects.
Still further objects of the ""901 invention provide for normalizing the first and second images before subtracting them to generate the third image. In this aspect, the invention determines distributions of intensity values of each of the first and second images, applying mapping functions to one or both of them in order to match the extrema of those distributions. The first and second images can also be registered prior to subtractions.
According to further aspects of the ""901 invention, the first and second images are generated by illuminating the die surface with different respective light or emission sources. This includes, for example, illuminating it with direct, on-axis lighting to generate the first image, and illuminating it with diffuse, off-access or grazing light to generate the second image. Additional aspects of the ""901 invention provide methods incorporating various combinations of the foregoing aspects. Although generally accurate under certain circumstances, each embodiment of the ""901 invention has disadvantages in certain other circumstances as described below along with disadvantageous of other prior art.
During die bonding manufacturing operations, a particular quantity of epoxy must be used to secure a die to a lead frame. As previously discussed, excess epoxy would frequently contaminate the surface of the die thereby reducing yield by causing short circuits on the die or by interfering with wire bonding to the die pads. Contrarily, bonding between the die and the lead frame may not be strong enough if too little epoxy is used. Measurement of the width of an adhesive bead surrounding a semiconductor die (xe2x80x9cadhesive wet-outxe2x80x9d or xe2x80x9cAWOxe2x80x9d) is a common method of confirming that a proper quantity of adhesive is used to secure a die to a lead frame. A bead width measurement that exceeds a specified limit indicates that too much adhesive was deposited. Measurement of a thin bead or detection of an adhesive void indicates that too little adhesive was deposited. Such measurements require finding the edges of the die.
Methods according to the prior art do not explicitly find the edges of the die. They assume the die to be of a fixed size even though the die may be a somewhat different size, for example, if the die was improperly diced. Therefore it also desirable to provide scale information along with displacement and rotation information that is necessary to perform adhesive wet-out measurements. Methods according to the prior art generally do not provide such scale information.
Non-linear variations in an image occur, for example, when lighting or other process variables are changed. A changing appearance of a reflection or refraction patterns on a semiconductor die may be caused by even slight tilting of the die. The methods according to the prior art that are previously discussed are embodiments of area based inspection techniques. Such area based techniques are ill suited to process images having such non-linear variation.
Although certain semiconductor die inspection methods according to the prior art may be generally accurate and efficient in many circumstances they have disadvantages in other circumstances. For example, a system that requires multiple light sources may be prohibitively expensive because additional costs are incurred in designing the lighting arrangement, purchasing and installing the additional lighting source. Furthermore, such systems may require additional registration procedures to be performed if a die has moved between acquisition of a first and a second image.
The methods according to the prior art are incapable of tolerating any substantial rotation of a die under inspection. Such a limitation is particularly disadvantageous for use in certain manufacturing equipment wherein substantial rotation of a die is a common occurrence. Furthermore the methods according to the prior art detect rotation of die by operating on at least two specific areas, typically opposite corners, of the die. Such a multiple point method of detecting rotation requires generation, storage and processing of multiple models and thereby increases processing time and wastes processor and memory resources.
The present invention provides a machine vision method of inspecting an object of manufacture which is tolerant to scale and rotation variations of the object. More particularly, the present invention provides a method of inspecting a semiconductor die surface for defects, inspecting lead areas of a semiconductor lead frame for defects and inspecting adhesive wet-out surrounding a semiconductor die in a lead frame assembly.
A training image is acquired and a training model is generated which may include an entire die at a coarse resolution or a specific region of interest at a finer resolution. A runtime image is acquired and compared with the training model using Rotation Invariant, Scale Invariant (RISI) tools to produce a measurement of rotation angle and scale of the runtime image relative to the training model. The RISI tool uses a one-point alignment method to measure the rotation angle and scale of the runtime image. The high accuracy angle measurement may be input to an Affine Transform component which rotates the training model relative to the runtime image or which rotates the runtime image relative to the training model. The post-transform runtime image is then inspected for defects by comparing it to the training model and seeking discrepancies between the two. Optionally, a GTC operation may be performed in parallel with the post-transform comparison and the results of each comparison may be merged. The results of the comparison(s) may be input to a morphological filter and/or a blob filter to eliminate noise and to accentuate features.
Accurate measurement of the width of an epoxy bead surrounding the semiconductor die requires knowledge of the true scale of the die in the runtime image. The scale measurement output from the RISI tool may be used to aid accurate measurement of adhesive wet out (AWO).
The method according to the present invention may be implemented to inspect semiconductor dies within a lead frame assembly or to inspect the lead frame assembly, or both.
Features of the present invention include improved methods for machine vision, and more particularly, improved methods for inspecting surfaces of semiconductor dies and lead frames. Increased accuracy and efficiency is achieved by operating on geometric attributes such as edges, boundaries and features rather than pixel based attributes such as those used in GTC, or normalized correlation methods. The invention features increased alignment accuracy, robustness, and tolerance to non-linear image variations because it applies edge based image-processing techniques. The invention also features improved inspection sensitivity because it does not mask areas of high frequency variation. The increased accuracy of alignment provided by the present invention is also at least partly due to use of geometric matching as opposed to pixel based schemes.
Further features of the present invention include a method of inspecting semiconductor die surfaces for defects using a minimum amount of inspecting equipment and having minimum setup requirements. For example, the methods according to the present invention do not require design and setup of multiple lighting sources.
The present invention also features a method of inspecting semiconductor dies and lead frames that is tolerant to variation in component rotation and scale. The present invention increases capability and efficiency of rotation and scale compensation because it does not require generation and storage of extra incrementally rotated models. The invention also increases efficiency by operating on a single point (local area) to determine alignment and angle of rotation thereby eliminating any need for a processing a second alignment point.
Additional features of the invention include a method of determining and using scale information of an image to locate a semiconductor die relative to a lead frame. The invention also features a method of using scale information to accurately measure adhesive wet-out.